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Automate Industrial Inspections with Vision AI Software

Quality Inspection wth Brain Builder

Improve your quality inspection process

Neurala’s vision AI software helps manufacturers improve their visual inspection and quality control processes to increase productivity, providing flexibility to scale to meet fluctuating demand. Easy to set up and integrate to existing hardware, Neurala software reduces product defects while increasing inspection rates and preventing production downtime – all without requiring previous AI expertise.

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Innovation that's both easy and flexible

Every quality inspection process is unique to the product being inspected. Neurala’s vision AI trains on fewer data samples than traditional AI, making it more flexible for product and line changes. Our Brain Builder™ software easily integrates with industrial automation applications and you can deploy your vision AI model to meet your business needs - whether you want to want to send image data to the cloud to be analyzed or keep that data on the factory floor with our Vision Inspection Automation (VIA) software.

Neurala VIA Diagram
Quality Inspection Production Line

Beyond machine vision

Defects that are easy for a human to see can be difficult for machine vision to detect. Adding vision AI dramatically increases your ability to detect challenging defect types such as surface anomalies and product variability. Neurala VIA helps improve inspection accuracy and the percentage of products being inspected. You can build data models in minutes, then easily modify and redeploy for changes on the production line.

Anomaly Recognition

Neurala’ s patented technology enables defective product detection using only samples of “acceptable” products. Anomaly recognition identifies any product that deviates from “acceptable”, allowing users to implement vision AI with extremely small amounts of data, and without defining hundreds of defect features. This saves you significant time and money building your custom vision AI solution.

Neurala VIA Anomaly-1

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